scholarly journals A CNN-LSTM BASED DEEP NEURAL NETWORKS FOR FACIAL EMOTION DETECTION IN VIDEOS

2021 ◽  
Vol 7 (1) ◽  
pp. 11-20
Author(s):  
Arnold Sachith A Hans ◽  
Smitha Rao
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Facial Emotion ◽  
Emotion Detection

Usage of Convolutional Neural Networks in Real-Time Facial Emotion Detection

2021 ◽  
pp. 259-273
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Sarat Chandra Nayak ◽  
Satchidananda Dehuri
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Facial Emotion ◽  
Emotion Detection

Voice Based Emotion Detection using Deep Neural Networks

2021 ◽  
Author(s):  
R Vijaya Saraswathi ◽  
Dheeraj Nandigama ◽  
R Vasavi ◽  
B Ganesh Babu ◽  
D Sai Shivani
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Emotion Detection

scholarly journals IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1289
Author(s):  
Navjot Rathour ◽  
Sultan S. Alshamrani ◽  
Rajesh Singh ◽  
Anita Gehlot ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Real Time ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Human Beings ◽  
Emotion Detection ◽  
Resource Constrained

Facial emotion recognition (FER) is the procedure of identifying human emotions from facial expressions. It is often difficult to identify the stress and anxiety levels of an individual through the visuals captured from computer vision. However, the technology enhancements on the Internet of Medical Things (IoMT) have yielded impressive results from gathering various forms of emotional and physical health-related data. The novel deep learning (DL) algorithms are allowing to perform application in a resource-constrained edge environment, encouraging data from IoMT devices to be processed locally at the edge. This article presents an IoMT based facial emotion detection and recognition system that has been implemented in real-time by utilizing a small, powerful, and resource-constrained device known as Raspberry-Pi with the assistance of deep convolution neural networks. For this purpose, we have conducted one empirical study on the facial emotions of human beings along with the emotional state of human beings using physiological sensors. It then proposes a model for the detection of emotions in real-time on a resource-constrained device, i.e., Raspberry-Pi, along with a co-processor, i.e., Intel Movidius NCS2. The facial emotion detection test accuracy ranged from 56% to 73% using various models, and the accuracy has become 73% performed very well with the FER 2013 dataset in comparison to the state of art results mentioned as 64% maximum. A t-test is performed for extracting the significant difference in systolic, diastolic blood pressure, and the heart rate of an individual watching three different subjects (angry, happy, and neutral).

scholarly journals Facial Emotion Detection through Deep Covolutional Neural Networks

2018 ◽  
Vol 15 (3) ◽  
pp. 113
Author(s):  
Aymun Saif Dar ◽  
Sheraz Naseer ◽  
Aihtshan Ali ◽  
Ishmal Sauf ◽  
Muhammad Ahsan
Keyword(s):  
Neural Networks ◽  
Facial Emotion ◽  
Emotion Detection

Enhanced speech emotion detection using deep neural networks

2018 ◽  
Vol 22 (3) ◽  
pp. 497-510 ◽  
Author(s):  
S. Lalitha ◽  
Shikha Tripathi ◽  
Deepa Gupta
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Emotion Detection
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